Developing a model of participation post-stroke: a mixed-methods approach
Bibliographic record
Abstract
PURPOSE: To develop and verify a model of participation post stroke, as a domain of health related quality of life. METHODS: An explanatory sequential mixed-methods design was selected. The quantitative phase developed a participation model with data from an observational study of 453 participants at 12 months post-stroke, using structural equation modeling. The qualitative phase followed to verify the model from the perspective of individuals post-stroke. Data was collected from two groups of eight stroke survivors involved in a multicentre trial about participation in the community. Individuals took photographs to describe participation; discussion of photographs was conducted over the course of three focus groups, and analysis identified emergent categories related to the model. RESULTS: The 12-month post stroke participation model consisted of latent variables: accomplishment, restricted roles, and health efficacy. The model fit was reasonable: normed χ(2) = 2.95, RMSEA = 0.066(0.052; 0.079). The qualitative data verified the model; participants initiated photo-taking and discussion of all aspects of the model. Concepts of social support, environment, and cognitive difficulties were also discussed in relation to participation. CONCLUSIONS: The participation model was developed and verified. Additions to future models are suggested. Theoretical, clinical, and research implications are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".